{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主要内容:  \n",
    "- 原理,优缺点,参数K取值对性能的影响\n",
    "- 处理分类问题案例\n",
    "- 处理回归问题案例\n",
    "- 糖尿病检测案例\n",
    "- 基于统计学的特征选择\n",
    "- 性能优化.\n",
    "- 卡方检查和F值检测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.算法原理\n",
    "\n",
    "k-近邻算法的核心思想是未标记样本的类别,由距离其最近的k个邻居投票来决定.    \n",
    "假设X_test为待标记的数据样本,X_train为已标记的数据集,算法的伪代码如下:  \n",
    "- 遍历X_train中的所有样本,计算每个样本与X_test的距离,并把距离保存在Distance数组中.\n",
    "- 对Distance数组进行排序,取距离最近的k个点.记为X_knn\n",
    "- 在X_knn中,统计每个类别的个数.即class0在X-knn中有几个样本,class1在X_knn中有几个样本等.\n",
    "- 待标记样本类别,就是X_knn中,样本个数最多的那个类别\n",
    "\n",
    "优缺点:\n",
    "- 准确性高,对异常值和噪声有较强的容忍度,\n",
    "- 计算量大.耗时\n",
    "\n",
    "算法参数:  \n",
    "算法参数是K,参数选择需要根据数据来决定,k越大,模型的偏差越大,噪声对数据越不敏感,但是可能造成欠拟合.k越小,模型的方差会越大,可能会造成过拟合."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分类问题示例\n",
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers=[[-2,2],[2,2],[0,4]]\n",
    "X,y=make_blobs(n_samples=60,centers=centers,random_state=0,cluster_std=0.60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "c=np.array(centers)\n",
    "plt.scatter(X[:,0],X[:,1],c=y,s=100,cmap='cool') # 画出样本\n",
    "plt.scatter(c[:,0],c[:,1],s=100,marker='^',color='orange') # 画出中心点\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k=5\n",
    "clf=KNeighborsClassifier(n_neighbors=k)\n",
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sample=np.array([0,2]).reshape(1,-1)\n",
    "y_sample=clf.predict(X_sample);\n",
    "neighbors=clf.kneighbors(X_sample,return_distance=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16, 20, 48,  6, 23]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出示意图\n",
    "plt.figure(figsize=(16, 10))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=100, cmap='cool')    # 样本\n",
    "plt.scatter(c[:, 0], c[:, 1], s=100, marker='^', c='k')   # 中心点\n",
    "plt.scatter(X_sample[0][0], X_sample[0][1], marker=\"x\", \n",
    "            s=100, cmap='cool')    # 待预测的点\n",
    "\n",
    "for i in neighbors[0]:\n",
    "    # 预测点与距离最近的 5 个样本的连线\n",
    "    plt.plot([X[i][0], X_sample[0][0]], [X[i][1], X_sample[0][1]], \n",
    "             'k--', linewidth=0.6);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用k-近邻算法进行回归拟合\n",
    "\n",
    "分类问题的预测是离散的,我们也可以使用k-近邻算法在连续区间内对数值进行预测."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_dot=40\n",
    "X=5*np.random.rand(n_dot,1)\n",
    "y=np.cos(X).ravel()\n",
    "y+=0.2*np.random.rand(n_dot)-0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(X)\n",
    "# print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练模型\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "k=5\n",
    "knn=KNeighborsRegressor(k)\n",
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如何进行回归拟合？ 在X轴上指定的区间内生成足够多的点，针对这些足够多的点，\n",
    "#使用训练处理的模型进行预测。得到预测结果y_pred，然后再坐标轴上，把这些\n",
    "# 连接起来，这样就画出来拟合曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "T=np.linspace(0,5,500)[:,np.newaxis]\n",
    "y_pred=knn.predict(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9779393525852534"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4800x3000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10),dpi=300)\n",
    "plt.scatter(X,y,c='g',label='data',s=100)\n",
    "plt.plot(T,y_pred,c='k',label='prediction',lw=4)\n",
    "plt.axis('tight')\n",
    "plt.title(\"KNeighborsRegresor (k=%i)\" % k)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 糖尿病预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"datasets/pima-indians-diabetes/diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数据说明：**\n",
    "- Pregnancies', 怀孕此时\n",
    "- 'Glucose',血浆葡萄糖浓度\n",
    "- 'BloodPressure', 舒张压\n",
    "- 'SkinThickness',肱三头肌皮肤褶皱厚度\n",
    "- 'Insulin',两小时内的血清胰岛素\n",
    "- 'BMI', 身高质量指数，体重除以身高的平方。\n",
    "- 'DiabetesPedigreeFunction',糖尿病血统指数\n",
    "- 'Age',年龄\n",
    "- 'Outcome'：阴性和阳性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本阳性和阴性个数\n",
    "data.groupby(data.Outcome).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分训练集和测试集\n",
    "X=data.iloc[:,0:8]\n",
    "Y=data.iloc[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 8)\n",
      "(768,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型比较\n",
    "# 使用普通的k-均值算法，带权重的k-均值算法，指定半斤的k-均值算法，分别进行拟合并评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name:KNN;  score:0.6948051948051948\n",
      "name:KNN with weight;  score:0.6883116883116883\n",
      "name:Radius Neighbors;  score:0.6623376623376623\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier,RadiusNeighborsClassifier\n",
    "\n",
    "models=[]\n",
    "models.append((\"KNN\",KNeighborsClassifier(n_neighbors=2)))\n",
    "models.append((\"KNN with weight\",KNeighborsClassifier(n_neighbors=2,weights=\"distance\")))\n",
    "models.append((\"Radius Neighbors\",RadiusNeighborsClassifier(n_neighbors=2,radius=500.0)))\n",
    "\n",
    "# 训练三个模型，并计算分\n",
    "result=[]\n",
    "for name,model in models:\n",
    "    model.fit(X_train,y_train)\n",
    "    result.append((name,model.score(X_test,y_test)))\n",
    "\n",
    "for i in range(len(result)):\n",
    "    print(\"name:{};  score:{}\".format(result[i][0],result[i][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 权重算法，这里使用了距离，距离越近，权重越高\n",
    "- RadiusNeighborsClassifier使用的半径，这里设置了500\n",
    "- 从分值看，普通的KNN分值效果是好一些的，但是因为训练和测试样本是随机分配的，不同的训练和测试样本，结果是不一样的。重复运行代码，分值也是变化的。\n",
    "- 为了让结果更加准确，可以使用多次随机分配训练数据集和交叉验证数据集，然后求模型的准确性均值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: KNN ,cross val score:0.7147641831852358\n",
      "name: KNN with weight ,cross val score:0.6770505809979495\n",
      "name: Radius Neighbors ,cross val score:0.6497265892002735\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "result=[]\n",
    "for name,model in models:\n",
    "    kfold=KFold(n_splits=10)\n",
    "    cv_result=cross_val_score(model,X,Y,cv=kfold)\n",
    "    result.append((name,cv_result))\n",
    "# KFold 把数据集分成了10份，1份用于交叉验证数据集来计算模型的准确性，9份用于训练，\n",
    "# cross_val_score()函数总共计算出10次不同训练集和交叉验证数据集组合得到的模型准确性评分\n",
    "# 计算分值平均值作为最后的输出\n",
    "for i in range(len(result)):\n",
    "    print(\"name: {} ,cross val score:{}\".format(result[i][0],result[i][1].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=2, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8355048859934854"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6948051948051948"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从分值看，模型对样本的拟合不是很好，过于简单，无法很好的拟合样本\n",
    "# 模型的准确性欠佳，预测的准确性不高。\n",
    "# 使用学习曲线证实"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from common.utils import plot_learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=2)\n",
    "cv=ShuffleSplit(n_splits=5,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\soft\\\\Anaconda\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6),dpi=300)\n",
    "plot_learning_curve(plt,knn,\"Learn Curve for KNN Diabetes\",X,Y,ylim=(0.0,1.01),cv=cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图可以看到，训练样本的分值是比较低的，且测试样本和训练样本的距离比较大。这是一个典型的欠拟合现象。  \n",
    "（k-均值算法没有更好的措施去解决欠拟合问题，可以使用其他的去提高这个模型的拟合效果）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征选择和数据可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征过多，也就是维度很多，高维度的数据特征是不便于直接看出来的。因而特征选择是有必要的。也就是说，选择2个与输出值相关性最大的特征。这样就可以比较容易的去观察。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "selectos=SelectKBest(k=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new=selectos.fit_transform(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148. ,  33.6],\n",
       "       [ 85. ,  26.6],\n",
       "       [183. ,  23.3],\n",
       "       [ 89. ,  28.1],\n",
       "       [137. ,  43.1],\n",
       "       [116. ,  25.6],\n",
       "       [ 78. ,  31. ],\n",
       "       [115. ,  35.3],\n",
       "       [197. ,  30.5],\n",
       "       [125. ,   0. ]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: KNN ,cross val score:0.725205058099795\n",
      "name: KNN with weight ,cross val score:0.6900375939849623\n",
      "name: Radius Neighbors ,cross val score:0.6510252904989747\n"
     ]
    }
   ],
   "source": [
    "result=[]\n",
    "for name,model in models:\n",
    "    kfold=KFold(n_splits=10)\n",
    "    cv_result=cross_val_score(model,X_new,Y,cv=kfold)\n",
    "    result.append((name,cv_result))\n",
    "# KFold 把数据集分成了10份，1份用于交叉验证数据集来计算模型的准确性，9份用于训练，\n",
    "# cross_val_score()函数总共计算出10次不同训练集和交叉验证数据集组合得到的模型准确性评分\n",
    "# 计算分值平均值作为最后的输出\n",
    "for i in range(len(result)):\n",
    "    print(\"name: {} ,cross val score:{}\".format(result[i][0],result[i][1].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 虽然效果和之间的差不多，但是也说明了特征选择的效果和总体的效果是差不多的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5),dpi=300)\n",
    "plt.ylabel(\"BMI\")\n",
    "plt.xlabel(\"Glucose\")\n",
    "plt.scatter(X_new[Y==0][:,0],X_new[Y==0][:,1],c='r',s=20,marker='o')\n",
    "plt.scatter(X_new[Y==1][:,0],X_new[Y==1][:,1],c='g',s=20,marker='^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看到，阴性和阳性的样本几乎都融合到了一起，这样的场景，并不是很适合K-近邻算法去进行预测的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如何提高k-近邻算法的运算效率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从算法原理看,每次需要预测一个点的时候,我们都需要计算训练集数据集里每个点到这个点的距离,然后选出距离最近的k个点进行投票,当数据集很大的时候,这个计算成本是很大的.针对N个样本,D个特征的数据集,其算法复杂度是O(DN^2).  \n",
    "为了解决这个问题,有一个K-D Tree 的数据结构,为了避免每次都重新计算一下聚集,距离信息会保存到一个树里.(如果A和B距离很远,B和C距离很近,那么A和C的距离也很远)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 相关性测试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设检验: 判断假设的结论是否成立或成立的概率有多高.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
